Spatially-optimized urban greening for reduction of population exposure to land surface temperature extremes

The population experiencing high temperatures in cities is rising due to anthropogenic climate change, settlement expansion, and population growth. Yet, efficient tools to evaluate potential intervention strategies to reduce population exposure to Land Surface Temperature (LST) extremes are still lacking. Here, we implement a spatial regression model based on remote sensing data that is able to assess the population exposure to LST extremes in urban environments across 200 cities based on surface properties like vegetation cover and distance to water bodies. We define exposure as the number of days per year where LST exceeds a given threshold multiplied by the total urban population exposed, in person ⋅ day. Our findings reveal that urban vegetation plays a considerable role in decreasing the exposure of the urban population to LST extremes. We show that targeting high-exposure areas reduces vegetation needed for the same decrease in exposure compared to uniform treatment.


INTRODUCTION
First paragraph: -Cities typically have lower albedos than rural areas. Some typical urban materials often have lower albedos, e.g. asphalt, many roofing types.
-Frequency of extreme heat is mostly controlled by synoptic weather patterns, not by the composition of the urban landscape. Third paragraph: -LST does not effectively capture the effects of shade from trees, since shade reduces temperatures of surfaces under trees that are not accessible to satellite-based remote sensors; therefore, it is not clear why the authors discuss tree shade. the results and Figure S2) that exposure to extreme heat occurs (as per this definition) when LST is higher than the 95th percentile (as defined within one city) based on average(?) LSTs and I am assuming that exposure is "reduced" whenever temperatures fall below the temperature for the 95th percentile as predicted by the statistical model for an increase in NDVI. If I am correct about the definition of exposure, I see certain limitations regarding exposure and the potential to reduce it for the urban population when increasing vegetation cover (NDVI). I think these limitations should be discussed in the manuscript and I would suggest to make a less generalized claim than saying that an increase of 3% of vegetation can reduce exposure to extreme heat by 50%.
1.) There is now quite an extensive body of literature and controversy about the usefulness of LST in studies about the urban heat island and the reduction of urban heat (e.g. Martilli et al. 2020). While I would not say that LST data is useless, I think that we should be aware of certain limitations when defining exposure based on LST: LST can only serve as a proxy for air temperatures and e.g. human thermal comfort. For example, during the day the canopy urban heat island (~2m air temperature difference) is often quite small (Oke et al., 2017). One explanation seems to be (simply put) that the city shapes it's own atmospheric boundary layer in such a way that heat can be transported more efficiently into higher layers of the atmosphere. This seems to be one of the reasons why air temperatures (during the day) over heavily sealed and high-rise building areas are sometimes not much higher than over vegetated land outside of the city. This can be problematic for two reasons: (1) The spatial pattern of the canopy urban heat island (i.e. air temperatures) may overall (but oftentimes not) correspond to the pattern of LSTs. This means the areas exposed to the highest LST (>95th) may not always be the ones exposed to the highest air temperatures. I think this should be discussed, since we may argue that air temperature matters often much more. (2) While the LST distribution ranges between, e.g., 25 and 35 °C, the air temperature in cities during the day is often not varying spatially by more than 0-2°C. Assuming that a heatwave in a changing climate may be, e.g., 2°C hotter (compared to some baseline), this may mean that the exposure to extreme heat and the 95th percentile should have a much broader definition than it has now (including a time dimension)? I think that this should be discussed and I think it would be great to have a much broader discussion in the paper why the author's think that their definition of exposure is useful and not merely arbitrary. For example, why is exposure defined per city and not globally, why not with a fix temperature threshold etc.

2.)
The diurnal cycle is also something crucial to consider. My understanding is that the MYD11A1 product has nighttime and daytime temperature in it, but that only daytime was used. During nighttime, the effects of vegetation can of course be quite small. Some studies even suggest that, e.g., trees could cause warming (depending on the urban setting). Exposure to extreme heat in this study is thus very narrowly defined as exposure to extreme heat during the day. However, the exposure to extreme heat during the night is at least equally if not more relevant (for example for human health).
3.) The areas with the highest population density may often be somewhere in or close to the city center and with a lot of high-rise buildings. There are two things that come to my mind that should probably be considered and discussed: (1) While the MODIS satellite/sensor will often see overall high LSTs for these areas, there are some places within these areas where temperatures are actually low. For example, in deep street canyons there is often a lot of shade. In these canyons the effect of vegetation is often relatively small (e.g. trees will not provide additional shade). Vegetation may actually not have that much of an effect here and of course (especially during night) trees may reduce cold air flows into the cities because they are natural obstacles. This may be especially relevant in densely built areas with a small amount of wind corridors. (2) When thinking of exposure to extreme heat, we could think of pedestrians and how they are exposed to heat. I think it is probably a valid assumption that there are a lot of pedestrians in highly populated areas and because of this many pedestrians may be exposed to heat (unless they walk in heavily shaded deep canyon streets etc.). However, another dimension is that people are exposed to heat within buildings. In buildings there is often air conditioning. So the number of people that (and during how much time of the day) are exposed to extreme heat, may depend very much on the availability of air condition. There might be strong gradients in air conditioning between wealthy and poor neighborhoods (from the city center to the outskirts). Even though I am not that familiar with the topics of exposure and vulnerability, it seems to me that these things would matter and that it may even become an issue of social justice in which neighborhoods it makes sense to increase vegetation and just looking at population density may be too simple. In addition, it is of course a huge topic, whether vegetation can even have an influence on temperatures within buildings and how much vegetation effects are only occurring very close to the vegetation itself. So assuming that a grid cell would profit (the whole grid cell) from a temperature reduction is often not correct, since only the areas with vegetation will experience a significant temperature decrease, but the urban areas close to these vegetated areas will profit much less.
Minor comments (somewhat unordered because there was no line numbering, which makes it really hard to comment): 1. In the first paragraph it says "with higher albedo". I think it is more often argued that cities have a lower albedo or that it is very dependent on the building materials deployed when constructing cities in different parts of the world.
2. First page, last paragraph: "Changes in the global, …, and allergic diseases". I don't think that this fits very well here. Maybe it can be integrated somewhere at the beginning of the paragraph or be removed.
3. Under the regression model: You say it is a multivariate regression model, but you only have one dependent variable. I am not sure that this is and should be called multivariate. At least, including multiple independent variables does in my opinion not make it multivariate.
4. I would assume that NDVI and NDBI have a strong negative correlation. How does that affect the estimated regression coefficients? I am missing a bit more information about the structure of the variables (correlation, distribution etc.) used and if there could indeed be issues with collinearity.
5. In addition, I would find it helpful to see a bit of model diagnostics. For example, how do the residuals look like? Are they normally distributed (normal q-q), what about homoscedasticity and outliers (e.g., residuals vs leverage)?
6. I have to say that I find the statistical approach overall a bit (maybe sometimes unnecessarily) complicated and sometimes the benefits are not entirely clear to me: a) The authors are saying that: "Our analysis shows that the model performs significantly better than a standard linear regression model, underlying the importance of the spatial dimension". They also claim that they don't use any "additional climate information". I consider both statements rather misleading (but of course I may also not fully understand everything). (1) The fact that there is a large improvement when using a "spatial model" seems due to the fact that you include some form of "auto-covariate" which is constructed based on the temperature itself. In my opinion this is "additional climate information" even though it happens to be the dependent variable in the model. (2) I believe that spatial autocorrelation when analyzing LST does not matter that much. I would assume that the large performance improvement in comparison to a "standard regression" approach, would also occur, if you simply include average LST per city as an additional independent variable. I also doubt that accounting for auto-correlation is most important in terms of "performance" (R2). Usually, I would consider it relevant to account for autocorrelation when you do some sort of statistical inference including the calculation of p-values and to a certain degree it can of course influence your regression coefficients. So to me it would make more sense to show these things (change in p-value and regression coefficients) instead of all the figures illustrating the performance. b) I am assuming that you are using the cross-validation approach to find an optimal "rho" value (which is a hyperparameter that controls, e.g., overfitting?), but I am not entirely sure if this is described explicitly and if my understanding is correct. Wouldn't it make sense to do the whole cross-validation exercise to find an optimal rho, then estimate the regression coefficients (for a combined training and validation set) and then evaluate the error with the help of the test set? I would say that averaging the regression coefficients (betas) should not have any benefits. Do you systematically vary "rho" to find its best value? If not and if "rho" is fitted as a parameter of the model, then I would again say that the whole cross-validation approach that you are employing does maybe not make much sense because the parameters will probably not be any better than without the cross-validation exercise.
6.) I guess the assumption that the NDVI has the same effect on temperature in different cities is more or less valid. On the other hand, I would assume that vegetation in dry regions use less water for transpiration and hence cause less cooling. Would a discussion on this make sense and did you check how much the regression coefficient of NDVI might vary across different cities? 7.) In Figure 10 I would find it more helpful to see boxplots in the left part. The right part of the figure is totally unclear to me. Probably the figure caption should be more explanatory.
8.) In the methods "Application of the model and evaluation of the mitigation strategy". The title of this section/paragraph should change. There is a lot of description about the cross-validation procedure etc. This could be an additional paragraph or be included under "The spatial model". Thank you for the opportunity to review this manuscript. I applaud the authors for their selection of cities across the world, both Global North and South, and the inclusion of diverse geographies and climates. While the manuscript's methods are clearly presented, the overall framing of the manuscript and interpretation of results should be strengthened. Most critically, the manuscript currently frames land surface temperature (LST) as essentially equivalent to heat exposure. While the authors could argue LST can be used as a proxy of sorts for heat exposure, they must acknowledge the well-documented limitations of LST. This framing also influences the interpretation of the results, which need much more nuanced than currently written, particularly if the paper is to be relevant to and clearly understood by decisionmakers. I offer several overarching comments first, followed by more specific line feedback.
Overarching areas of improvement: • As stated above, a more nuanced discussion is needed in the introduction on the drawbacks and limitations of LST and the evidence of its relationship to heat exposure of community members. Community members have different heat exposure over time depending on the quality of housing, travel modes, and thermal safety at work or school, etc. If LST is to be used as a proxy for heat exposure, the authors should acknowledge the limitations and drawbacks of doing so. For  • In the introduction and discussion, I also recommend acknowledging other types of heat mitigation strategies available to decisionmakers to limit urban heat including cool/higher albedo materials and surfaces, urban design choices that can improve thermal comfort in microclimates, and the reduction of waste heat sources through the improved energy efficiency of buildings and indoor cooling as well as the reduction of gasoline-powered vehicles. • Along the same lines, the authors should be more nuanced in the discussion of urban greening strategies for heat mitigation, and mention the critical water resource tradeoff decisions that would need to be made in many drought-stricken areas of the world (particularly arid regions which they identify would need greater amounts of urban greening to reduce LST).
• Finally, the authors should address that it is not simply a matter of increasing the total amount of urban greening in a city, but where the greening is targeted. Gober et al. (2009) found that irrigated landscapes in an arid region lowered nighttime temperatures but only in the least vegetated neighborhoods. Similarly, there are numerous studies published about the relationship between LST and historically marginalized areas within cities (additional references below), so targeting interventions to those most vulnerable community members should also be considered. While the marginalized areas of cities are not part of your study, the inequity of where vegetation is most often already located should be addressed in the introduction and the interpretation of the results. o Gober, P., Brazel, A., Quay, R., Myint, S., Grossman-Clarke, S., Miller, A., & Rossi, S. (2009). Using watered landscapes to manipulate urban heat island effects: how much water will it take to cool Phoenix?. Journal of the American Planning Association, 76 (1) Specific line feedback: • Title: The manuscript is only focused on "urban greening strategies" versus a broader set of heat mitigation strategies, and the focus of the methods is on LST and the reduction of LST, rather than reduction of heat exposure. • Page 1, 2nd paragraph: "As a consequence, heat waves in urban climates will have a profound impact on humankind as the climate warms up." Please reflect upon the tense in which heat waves is framed in this sentence. Heat waves are already having an impact on individuals, a risk that is increasing (versus a risk that is only in the future). • Page 2, Paragraph 1: "To cope with the increasing need to foster climate mitigation and adaptation, it is therefore imperative to have spatially detailed, temporally sub-daily information for most, if not all cities." Not sure why this distinction is being made here, what cities do not need to cope with climate change? Recommend rephrasing to something like, "…information for cities." • Page 2, Paragraph 1: "…subject focused on the so-called Urban Heat Island (UHI) phenomena…" Recommend removing "so-called". • Page 3, Paragraph 1: "city thermoscape…" recommend continuing to use "urban thermoscape" as indicated in the introduction. • Page 3, Paragraph 1: "For instance, these plans could be implemented by favouring green spaces and constructing green or cool roofs and cool pavements." This statement is correct, but in the context of the paragraph the focus is urban greening strategies, of which cool roofs and cool pavements are not part of. Recommend rephrasing to clarify the distinction or removing these strategies from this sentence. • Page 4, Paragraph 1: "However, we are still missing dedicated modelling tools to facilitate the design of city-specific plans based on the magnitude of the intervention required to reach climate targets." Can you provide evidence from the literature that modelling tools are needed, and not training of professionals on existing modelling and information decision support tools? • Page 4, Paragraph 2: Noting it is being referred to as "city thermoscapes" again here. • Page 4, Paragraph 3: "It is evident that people in arid regions are located in areas of their particular city less exposed to extreme heat…" Please clarify and reconsider your interpretation of this result. The populations in arid regions may be located in areas of a city that have lower UHI relative to surrounding natural landscapes, but that does not mean they are less exposed to extreme heat. • Page 5, Paragraph 3: "we show the amount of vegetation we need to increase in order to reduce the exposure of the urban population to extreme heat areas…" More accurate to say that the scenario shows vegetation needed to reduce the LST, which you are then using as a proxy for exposure of the urban population to heat. • Page 5, Paragraph 3: "Whether this expansion of urban vegetation is actually feasible in that specific area without compromising the capacity to host people should be further investigated." Not only the capacity of the area to host people, but also trade-offs with water use, maintenance costs of the additional vegetation, etc. • Page 6, Paragraph 1: Although the study is focused on urban greening strategies, this would be another good place to nod to the fact that cities are not only pursuing greening, but have an additional suite of heat mitigation strategies available to them. As currently written, it sounds like the only option available to cities is urban greening and not a holistic mix of greening, cool surfaces, and waste heat reduction. • Finally, I also recommend including line numbers in future submissions of the manuscript.  We would like to thank a lot the three Reviewers that allow us to improve significantly the manuscript and the results of the 14 research. The major changes addressed are the following: 15 1. We changed the title as suggested.    5. We critically discussed the scope of our work and its possible integration with other intervention strategies. 25 6. We added the Data Availability and Code Availability sections and we shared a public repository that contains all the data 26 and the codes used in this research 1 . All the analysis and the figures we show here are in a dedicated python notebooks 27 called responseToReviewers.ipynb. 28 We would like to emphasize that the key contribution of this study is to establish a highly accurate Spatial Lag Model (SLM) 29 for predicting urban population exposure to extreme heat. We then use the model's results to evaluate the impact of vegetation 30 on reducing such exposure. We calculate exposure based on Land Surface Temperature (LST) data, which has been criticized 31 by some studies, including Martilli et al. 2020 2 , for its limitations in assessing the Surface Urban Heat Island (SUHI) effect.

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However, it's worth noting that our study doesn't focus on SUHI, which compares rural and urban LST, but rather on the 33 absolute value of extreme heat exposure, calculated pixel by pixel. We finally show that targeting high exposure areas reduces 34 vegetation needed for the same decrease in exposure compared to a uniform treatment. This submission combines spatially explicit land surface temperature (LST), land cover, and population data to assess the poten-42 tial for LST reduction via urban greening in populated areas across the main climate zones globally using a spatial regression 43 model. In general, it is well executed and presented, and there is potential to explain the results more clearly. However, while 44 this submission is scientifically interesting, its practical significance is in doubt. The authors do not discuss the disadvantages 45 of LST for practical purposes. I have suggested some references for the authors' consideration, and I suggest the authors 46 better communicate the uncertainty in terms of the practical relevance of their findings, especially in the abstract and conclusions. 47 We thank Reviewer 1 for appreciating our work from a scientific viewpoint and for his relevant comments that helped us to 49 significantly improve the manuscript. We fully agree with Reviewer 1 that we did not discuss sufficiently the limits and the 50 disadvantages of LST for this kind of intervention. We also agree that the practical significance of this work as it was before 51 could be questionable. The main goal of this research is to provide a model that is able to predict with high accuracy the 52 exposure to extreme heat in urban environments. We provided a global model, while models for singles cities based on spatial 53 regression should be developed for single cases and more in-depth practical purposes. We would like to emphasize that in the 54 revised version we changed the definition of exposure and we presented a geographical regression model that is able to predict 55 with high accuracy the number of days and nights over given thresholds. We used the parameters of the model to estimate the 56 increment of vegetation (NDVI) in order to reduce the exposure with a global comparison across different climate zones. Any 57 practical interventions should be analyzed locally for each city, and it is beyond the scope of this work, and we emphasized this 58 point further in the revised manuscript. In the revised manuscript we discussed these limitations in a dedicated section. In the 59 following, we tried to address all the comments made by Reviewer 1.

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We would like to emphasize that the key contribution of this study is to establish a highly accurate Spatial Lag Model    surface temperature. Moreover, in the biometeorological literature actual heat exposure is calculated (and related to far more 75 than longwave radiation, but also shortwave radiation/shade, air temperature, wind speed, humidity). Also, "thermoscapes"  In other words, while satellite-derived LST is readily available and therefore often used in these kinds of analyses, I don't think 87 it is the appropriate data to answer the scientific questions posed here. The link between LST and actual exposure of urban 88 residents is extremely tenuous at best. Moreover, for assessment of impacts of tree, in particular, it is not appropriate. The  We are grateful to Reviewer 1 for bringing this up. We concur with the reviewer that basing the definition of heat exposure 92 solely on satellite data can be limited at specific spatiotemporal scales. In the revised manuscript, we added a subsection in the Introduction where we discussed the limitations of using LST and emphasized that at the scale of our analysis, there is a 94 clear relationship between air temperature and remotely-sensed surface temperature 3, 4 . It's important to note that our study 95 2/24 focuses on global patterns and seasonal or longer timescales, not hourly/daily conditions at specific locations where differences 96 between air and surface temperatures could potentially be significant due to weather-scale processes, as noted by Martilli et al.

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(2020) 2 . Additionally, our work does not focus on the definition of Surface Urban Heat Island (the difference between rural and 98 urban areas), but rather on the actual values of LST without any relative comparison. This is because, as shown in Figure 1 of 99 the manuscript, we do not believe that this definition is representative of heat stress in cities, but rather reflects rural vegetation. is not part of this study. It is worth noting that currently, it is not feasible to obtain daily global information on air temperature 102 at the spatial scale of our work (i.e. 1 km) for inter-city comparisons. 103 We also agree on the fact that "thermoscapes" based only on 2-D surface temperatures is a limited definition and we 104 removed this term from the manuscript: the term was used because that was the aspiration and while we did not reach it we are 105 building research to go towards it.

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At the same time, while the major impact of trees and other plants in terms of heat exposure is shade 6 , we would like also to 107 emphasize the effect of evapotranspiration 7 . Additionally, vegetation can absorb and store heat, which can further decrease the 108 temperature in an urban area 8 . Furthermore, trees and vegetation can also reduce the amount of heat absorbed by buildings 109 and pavement surfaces, which can lower the temperature inside buildings as well 9 . Additionally, the cooling effects of urban 110 plants go beyond just providing shade for roads. One major way they help is by releasing water through evaporation, which 111 can absorb a significant amount of net radiation. By lowering the ratio of sensible heat to latent heat (known as the Bowen 112 ratio), plants can effectively lower air temperature and help combat climate change even in areas that aren't directly shaded.

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This can also have an indirect impact on nearby areas due to heat advection. Our data analysis shows high values of the spatial 114 autocorrelation of LST that is driven by these effects.

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-We revised this point in the paragraph.

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-Cities typically have lower albedos than rural areas. Some typical urban materials often have lower albedos, e.g. 128 asphalt, and many roofing types.

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-We revised this point in the paragraph emphasizing the albedo's properties of urban landscapes.

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-Frequency of extreme heat is mostly controlled by synoptic weather patterns, not by the composition of the urban 131 landscape.

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-We revised this point in the paragraph emphasizing the interplay between climate events and urban landscape.

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-We agreed with Reviewer 1 on this point and we, therefore, revised this part in the Introduction. We should also 138 mention that shade depends on the acquisition time. As we use Modis Terra which scans at 10:30 am in the morning, 139 the shadows are partly lateral and could be "seen" by the sensor. Arguably not so much for many trees if they are in 140 groups, but it would be the case for shades from buildings in urban canyons at some points. We believe that this 141 point should be studied and analyzed in further studies.

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We thank Reviewer 1 for the comments. We addressed them below • Distance to water bodies: the distance will be much more relevant if the water is upwind (for the dominant wind direction) than downwind; I presume this is not taken into account?

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• yes, we did not consider the wind direction and we emphasized this point in the revised manuscript.

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• Climate and population exposure to extreme heat 155 -I disagree that "exposure of the urban population to extreme heat" is calculated. It is the co-location of high 156 LST with population. High roof temperature, a primary contributor to LST, is unlikely to impact the population 157 except perhaps those living on the top floor, depending on building construction. Also, the "warmest areas of the 158 city" may be quite different if air temperature was the metric. Finally, "extreme heat" is usually used to indicate 159 synoptically-driven heat (e.g. heat waves), or at very least experiencing (air) temperature exceeding 90th+ percentile 160 of the *temporal* distribution of temperature. The 95th percentile of temperature spatially is not very relevant if 161 nowhere in the city is too hot.

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-We really thank Reviewer 1 for this point. Thanks to his comment we revised the definition of extreme heat areas 163 and therefore the exposure of the urban population to extreme heat. We aligned our definition with traditional ones 164 that defined the urban population exposure to heat as the number of days per year that exceed a heat exposure 165 threshold multiplied by the total urban population exposed 1, 10 . We measure exposure in person-days/year-1 which 166 is a widely used metric to compare and contrast exposure to extreme heat across geographies and time periods 11 . 167 We put a lot of effort into this point: in particular, for each city, we calculated the 90th+ percentile of the temporal year.

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The new definition of total exposure is: where we defined the total exposure (T E) as the average of the exposures between day (T E D ) and night (T E N ). In

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• Reduction of the population exposed to extreme heat.

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-What exactly is meant by "the population exposed to the warmest areas of the urban environments can be reduced 186 by 50%"? Does it meant, for the example of Paris (threshold of 34.4 C), that temperature is reduced to 34.35 187 C? The issue with the use of percentages above or below a threshold is that a small reduction in temperature that 188 nevertheless crosses a defined threshold (e.g., from 34.5 C to 34.3 C for Paris) is not that relevant or impactful.

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Even the significance of a 1 C LST reduction for the population is not clear, both in terms of the relevance of this We deleted the final 2 sentences.

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This study explores the potential exposure of urban populations to extreme heat under different greening scenarios. It relies on 208 an interesting approach of combining data about urban population and satellite-derived LSTs. The authors claim that increasing 209 the overall urban vegetation by 3% can already lead to reduction in exposure to extreme heat of 50%. I think that this statement 210 may be technically correct for a certain definition of exposure. However, I think that this definition of exposure may not be that 211 useful when trying to better understand how to protect the urban population from the harmful effects of heat in urban areas.

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I also think that the results are presented in a way that suggests an easy solution to reducing the detrimental effects of heat  We thank Reviewer 2 for emphasizing this point: this comment is very similar to one of the comments raised by Reviewer 1.

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We recognized that the previous definition of the exposure of the urban population to extreme heat was not really clear. For this 238 reason, according also to the comments made by the other Reviewers we revised our definition and therefore we revised our 239 results and our statements. We revised the definition of extreme heat areas and therefore the exposure of the urban population 240 to extreme heat. We aligned our definition with traditional ones that defined the urban population exposure to extreme heat as 241 the number of days per year that exceed a heat exposure threshold multiplied by the total urban population exposed 1 We are grateful to Reviewer 2 for bringing up the point about the limitations of using only satellite data to define heat 264 exposure. We concur that this method is limited in terms of spatio-temporal scales. In the revised manuscript, we 265 added a section in the Introduction to discuss the limitations of using land surface temperature (LST) data, but we also 266 emphasized that at the scale of our analysis, there is a clear relationship between air temperature and remotely-sensed 267 LST, as previously reported in the literature 3, 4 . It is important to note that our study focuses on global patterns and 268 seasonal or longer timescales and not on hourly or daily conditions at specific locations, where differences between air 269 and surface temperatures may be more pronounced due to weather-scale processes as emphasized by 2 . Additionally, 270 our study does not focus on the definition of the Surface Urban Heat Island as the difference between rural and urban 271 areas, but rather on the actual values of LST without any relative comparison. We acknowledge that this definition is not 272 representative of heat stress in cities and mainly reflects rural vegetation (see Figure 1 of the manuscript). We discussed 273 those points in a dedicated subsection in the Introduction of the revised manuscript. 274 We also agree areas exposed to the highest LST (>95th) may not always be the ones exposed to the highest air temperatures: We are grateful to Reviewer 2 for bringing attention to the definition of extreme heat areas and the exposure of the urban 302 population to such heat especially for suggesting to consider the diurnal cycle and to use both the MOD11A1 day and 303 night products. We have revised our definition to align it with traditional ones, which define urban population exposure to 304 heat as the number of days per year that exceed a specific heat exposure threshold multiplied by the total urban population 305 exposed. We use the widely accepted metric of person-days/year-1 1, 10 to compare and contrast exposure to extreme heat 306 across different locations and time periods 11 . 307 We have put significant effort into addressing this point by calculating the 90th+ percentile of the temporal distribution 308 of land surface temperature (LST) over a 20-year period (2000-2021) for each city. We also calculated the threshold 309 information for both daytime and nighttime for all cities and presented this information in Figure Rev1 and in Figure Rev2. 310 For each pixel, we calculated the number of days that we observed LST to be greater than or equal to the 90th percentile Our revised definition of total exposure is the average of the exposures between daytime and nighttime, which we have calculated using two spatial regression models that predict the number of days and nights over the thresholds with high accuracy. In particular, the new definition of total exposure is: where we defined the total exposure (T E) as the average of the exposures between day (T E D ) and night (T E N ). In  (2) When thinking of exposure to extreme heat, we could think of pedestrians and how they 330 are exposed to heat. I think it is probably a valid assumption that there are a lot of pedestrians in highly populated areas 331 and because of this many pedestrians may be exposed to heat (unless they walk in heavily shaded deep canyon streets 332 etc.). However, another dimension is that people are exposed to heat within buildings. In buildings, there is often air 333 conditioning. So the number of people that (and during how much time of the day) are exposed to extreme heat, may vegetation will experience a significant temperature decrease, but the urban areas close to these vegetated areas will profit 342 much less. 343 We appreciate the Reviewer's comments on the role of vegetation in cities.  (b) The Reviewer is correct that exposure to heat is not limited to the outdoors and that people may also be exposed to 362 heat within buildings. The availability of air conditioning, as well as the gradient in air conditioning between wealthy 363 and poor neighborhoods, is a crucial factor to consider when assessing exposure to extreme heat. Additionally, 364 the Reviewer raises a valid point that the effects of vegetation on building temperatures is a complex topic that 365 would need to be further studied. The Reviewer also highlights that considering population density alone may not 366 be enough when thinking about where to increase vegetation and that it is an issue of social justice. Overall, this 367 comment highlights the need for a multi-faceted approach when thinking about exposure to extreme heat and the 368 importance of considering the built environment and socio-economic factors when assessing vulnerability. Let us 369 say that currently we are not capable to doing so, but that such work could be envisaged in future developements of 370 the methods. It would rely on reliable spatialiazed socio-economic data, which may not be readily available for all 371 cities worldwide with the same resolution. We tried to answer this part in the introduction and in the conclusion 372 sections.

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Minor comments (somewhat unordered because there was no line numbering, which makes it really hard to comment): 374 1. In the first paragraph it says "with higher albedo". I think it is more often argued that cities have a lower albedo or that it 375 is very dependent on the building materials deployed when constructing cities in different parts of the world. 376 We thank the reviewer for this comment: indeed it was a mistake and we changed it.

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2. First page, last paragraph: "Changes in the global, . . . , and allergic diseases". I don't think that this fits very well here.

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Maybe it can be integrated somewhere at the beginning of the paragraph or be removed. 379 We agreed with Reviewer 2 with this comment and we moved to the previous part of the manuscript.   NDVI and NDBI as shown in Figure Rev5 and in Table 1 with a value of the person correlation of ρ 0.78. 390 We also report the value of the multicollinearity condition number which is a measure of the degree of multicollinearity in Figure Rev6.

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In the revised version, we chose to use only NDVI and distance to water bodies as predictors because for certain cities 397 we observed a strong negative correlation between NDBI and NDVI. This simplifies the model without affecting its 398 performance. Our aim is to demonstrate the impact of vegetation in reducing exposure to extreme heat.  We thank Reviewer 2 for this comment that allows us to prove that our methodology is valid. We analyzed the distribution 6. I have to say that I find the statistical approach overall a bit (maybe sometimes unnecessarily) complicated and sometimes the benefits are not entirely clear to me: a) The authors are saying that: "Our analysis shows that the model performs 410 significantly better than a standard linear regression model, underlying the importance of the spatial dimension". They 411 also claim that they don't use any "additional climate information". I consider both statements rather misleading (but 412 of course I may also not fully understand everything). (1) The fact that there is a large improvement when using a 413 "spatial model" seems due to the fact that you include some form of "auto-covariate" which is constructed based on the 414 temperature itself. In my opinion this is "additional climate information" even though it happens to be the dependent 415 variable in the model. (2) I believe that spatial autocorrelation when analyzing LST does not matter that much. I would 416 assume that the large performance improvement in comparison to a "standard regression" approach, would also occur, 417 if you simply include average LST per city as an additional independent variable. I also doubt that accounting for that you are using the cross-validation approach to find an optimal "rho" value (which is a hyperparameter that controls, approach that you are employing does maybe not make much sense because the parameters will probably not be any 429 better than without the cross-validation exercise. 430 We thank Reviewer 2 for this comment that allows us to prove that our methodology is valid.

431
• 1) We agree on the fact that somehow we add a sort of climate information in the model. We revised this part in the 432 manuscript and we removed the sentence. Our goal was to explain that we used only remote sensing data without 433 any other information like data from climate background.

434
• 2) Here we show the values of the performance of a linear regression model in terms of R 2 if we include average 435 LST per city in Figure Rev10. We can see that the performances of the linear regression models are very low even 436 if we consider the average LST (OLS 1 ) compared to the spatial ones, especially in the phase of cross-validation 437 where we get negative values of the R 2 .

438
• 3) We agree with Reviewer 2 that the main goal of spatial models is not their performance but rather that they are 439 able to explain autocorrelation. We report the value of the Moran's I coefficient in Figure Rev11. The analysis 440 shows very high values of the coefficients I > 0.9 and this means that is important to account for spatial models in 441 order to provide a better solution because there is a spatial dependence between the dependent and independent 442 variables. Indeed, spatial regression models account for this dependence to provide more accurate results 17 . This

446
• 4) Regarding the cross-validation part, we probably did not explain well the modus operandi we adopted. Let us say that for an individual observation, the spatially lagged equation can be written as the following: with j ̸ = i. Since the dependent variable, y appears on both sides of the expression: we can re-arrange this expression to solve for Y :

472
We added this reference and we discussed it.

11/24
Thank you for the opportunity to review this manuscript. I applaud the authors for their selection of cities across the world,

511
We thank the Reviewer 3 for this comment that helped us to really improve the manuscript. We added a subsection in the 512 introduction to discuss the limitations and advantages of LST to study the exposure of urban population to extreme heat. 513 We also changed our definition of expsosure to extreme heat.

514
• In the introduction and discussion, I also recommend acknowledging other types of heat mitigation strategies available 515 to decisionmakers to limit urban heat including cool/higher albedo materials and surfaces, urban design choices that 516 can improve thermal comfort in microclimates, and the reduction of waste heat sources through the improved energy 517 efficiency of buildings and indoor cooling as well as the reduction of gasoline-powered vehicles. 518 We thank the reviewer: we agreed with him/her and we added this part to the introduction 519 • Along the same lines, the authors should be more nuanced in the discussion of urban greening strategies for heat 520 mitigation, and mention the critical water resource tradeoff decisions that would need to be made in many drought-521 stricken areas of the world (particularly arid regions which they identify would need greater amounts of urban greening 522 to reduce LST). 523 We thank the reviewer: we agreed with him/her and we added this part to the introduction.

541
We thank the reviewer: we agreed with him/her and we tried to answer to this part in the introduction and in the conclusion 542 sections.

543
Specific line feedback:

544
• Title: The manuscript is only focused on "urban greening strategies" versus a broader set of heat mitigation strategies, 545 and the focus of the methods is on LST and the reduction of LST, rather than reduction of heat exposure. 546 We thank the Reviewer 3 on this point and we change the title focusing on the greening intervention strategy.

547
• Page 1, 2nd paragraph: "As a consequence, heat waves in urban climates will have a profound impact on humankind 548 as the climate warms up." Please reflect upon the tense in which heat waves is framed in this sentence. Heat waves are 549 already having an impact on individuals, a risk that is increasing (versus a risk that is only in the future). 550 We thank the Reviewer 3 on this point and we changed this part on the manuscript.

551
• Page 2, Paragraph 1: "To cope with the increasing need to foster climate mitigation and adaptation, it is therefore 552 imperative to have spatially detailed, temporally sub-daily information for most, if not all cities." Not sure why this 553 distinction is being made here, what cities do not need to cope with climate change? Recommend rephrasing to something 554 like, ". . . information for cities." 555 We thank the Reviewer 3 on this point and we changed this part on the manuscript.

556
• Page 2, Paragraph 1: ". . . subject focused on the so-called Urban Heat Island (UHI) phenomena. . . " Recommend 557 removing "so-called". 558 We thank the Reviewer 3 on this point and we changed this part on the manuscript.

561
We thank the Reviewer 3 on this point and we removed the term thermoscape in the manuscript that could lead to 562 misinterpretations.

563
• Page 3, Paragraph 1: "For instance, these plans could be implemented by favouring green spaces and constructing 564 green or cool roofs and cool pavements." This statement is correct, but in the context of the paragraph the focus is 565 urban greening strategies, of which cool roofs and cool pavements are not part of. Recommend rephrasing to clarify the 566 distinction or removing these strategies from this sentence. 567 We thank the Reviewer 3 on this point and we removed this part on the manuscript. As we removed the term thermoscape from the manuscript, we would like to say our goal would be to provide in future a 574 tool or methodology that is able to define a thermoscape of a city. In this research we hope to provide an initial approach 575 to this.

576
• Page 4, Paragraph 3: "It is evident that people in arid regions are located in areas of their particular city less exposed to 577 extreme heat. . . " Please clarify and reconsider your interpretation of this result. The populations in arid regions may be 578 located in areas of a city that have lower UHI relative to surrounding natural landscapes, but that does not mean they are 579 less exposed to extreme heat. 580 We are completely agree with this point that it is the reason why we do not want to study UHI bu the real value of heat 581 for each pixel (without any difference to rural urban areas). Our goal is to model and predict exposure to extreme heat. 582 We better explained this point in order to make sure that the reader could get the point.

583
• Page 5, Paragraph 3: "we show the amount of vegetation we need to increase in order to reduce the exposure of the urban 584 population to extreme heat areas. . . " More accurate to say that the scenario shows vegetation needed to reduce the LST, 585 which you are then using as a proxy for exposure of the urban population to heat.

586
In this revised version we show different results and we changed the text accordingly.

587
• Page 5, Paragraph 3: "Whether this expansion of urban vegetation is actually feasible in that specific area without 588 compromising the capacity to host people should be further investigated." Not only the capacity of the area to host people, 589 but also trade-offs with water use, maintenance costs of the additional vegetation, etc.

590
We really agreed with the Reviewer 3 on this point and we added this reflection on the manuscript.

591
• Page 6, Paragraph 1: Although the study is focused on urban greening strategies, this would be another good place to nod 592 to the fact that cities are not only pursuing greening, but have an additional suite of heat mitigation strategies available to       Figure Rev12. Schematic representation of the cross-validation phase.

REVIEWER COMMENTS
Reviewer #1 (Remarks to the Author): Reducing extreme heat exposure in cities through spatial analysis of urban greening by Massaro et al.
Comments on Response to Reviewer 1 The authors have done a reasonable job of revising the manuscript; however, I still take issue with several aspects of the current presentation. While in general the authors have executed a good study, the fundamental methods are not adequate for the purpose, in my view. I think Manoli et al. (2019), being published in such a high profile outlet (i.e. Nature), has unfortunately set back the understanding of heat exposure and heat stress in cities by readers who may not have the scientific context to understand the drawbacks or flaws in their method. I am opposed to any further such publications, and in its current state this submission claims that LST is useful in some practical sense to identify areas of increased urban heat exposure and the benefits of vegetation. Yet, there is little proof that this is so and a lot of clear reasons why we would expect it not to be. I do not want to see another high profile article (e.g. in Nature Communications in this case) conflate LST with the actual variables that impact heat exposure simply because LST is readily available and is therefore the basis of the method chosen.
A primary overall comment I have is that I think the use of the term "heat exposure" is not accurate. LST cannot be linked to heat exposure (indoor or outdoor) in a direct way. The use of this term when working with urban air temperature is more justified (but could still be questioned) because air is well mixed via turbulence, and therefore varies more slowly in space. Surface temperature, by contrast, varies at small spatial scales. Remotely-sensed surface temperature includes rooftop and tree top temperatures which are not of much relevance to outdoor heat exposure at ground level (pedestrians). I strongly recommend that the authors speak of surface temperature instead (and be clear about which surface temperature, e.g. see Stewart et al. 2021, Fig. 8). E.g., the title could be: "Reducing extreme surface temperature in cities through spatial analysis of urban greening" [Note: Manoli et al. (2020) make several comments about surface temperature being more useful than air temperature, which are only partially accurate and highly inaccurate if LST is the "surface temperature" metric they indicate. I suggest caution in using this non-peer reviewed publication by scientists who mostly have little urban climate background to support any claims.] This brings me to a second point. I don't think that spatial analysis of urban greening will reduce surface temperature (or heat exposure), but urban greening may well do so. Therefore, I suggest a title for this submission that is more accurate, such as: "Spatially-optimized urban greening for reduction of urban land surface temperature".
"We changed the definition of population exposure to extreme heat areas, which are now defined as the number of days when heat exposure surpasses a specific threshold multiplied by the total urban population.  Fig. 8.) "(ii) offer monitoring and modelling systems of the urban thermal environment (hereafter defined as the urban thermoscape)" This is very vague. The claim seems to be made that LST can capture the "urban thermal environment" or "urban thermoscape": "…the design and evaluation of urban adaptation plans should be city-specific and based on the detailed assessment of the city thermoscape. To asses the latter requirements, in this study we focus our attention on the drivers of the urban LST gradients across different climate zones. …" I think more precision is required -which specific temperature or combination of temperatures are included in the "urban thermal environment" or "urban thermoscape"? It seems that LST is the answer in this submission. Yet, LST clearly misses several elements of importance to the "urban thermal environment", e.g. building walls, ground underneath trees, and includes others of less consequence (e.g. rooftops, depending on the application) and it ultimately a biased measure of the "urban thermal environment". Finally, the English language requires improvement in some of the newly-written sections.
Reviewer #2 (Remarks to the Author): I thank the reviewers for their effort and I think that they substantially improved the manuscript. They acknowledge several limitations including the ones that stem from relying on LST. For me it would be important that it is clear from the beginning how exposure is defined in this study and how it should be understood.
This means that the definition in the abstract: "We determine exposure by calculating the number of days when heat exposure surpasses a specific threshold multiplied by the total urban population affected." should be replaced by the definition in the conclusions which is much clearer and much more meaningful "We defined exposure as the number of days per year where LST exceeds a heat exposure threshold multiplied by the total urban population exposed, in person days." In the abstract it also says: "Here, for the first time we implement a spatial regression model based on remote sensing data that is able to assess, with high accuracy, the population exposure to extreme heat in urban environments across 200 cities based on surface properties like vegetation cover and distance to water bodies." I don't agree with the statement "with high accuracy". This is purely based on the statistical fit as far as I understand. However, it does not pay attention to the fact that LST is a very specific and often inadequate indicator for "heat".
Reviewer #3 (Remarks to the Author): Thank you for the opportunity to review the revised manuscript. The revisions have greatly improved the study's overall framing and provided more nuance for its potential application in both research and practice. The discussion of the limitations of LST and references to other heat mitigation strategies, and the importance of considering indoor heat exposure, are welcome and better situates this study in current heat literature. My major feedback has been resolved, although I list several minor specific line comments below. Specific line feedback: • Line 52 (Figure 1): Consider a simple symbology for both Kyoto and Jeddah (since they are the cases listed) that help the reader identify them quickly on the world map and the graph.
• Line 156 ( Figure 4): Recommend briefly describing what Scenario 1 and 2 are in the caption (or giving them a more descriptive short-hand title) • Line 190 ( Figure 5): Recommend clarifying caption for "C" to again make Scenario 1 and 2 more clear, such as: "C) Global NDVI increment in order to achieve the exposure reduction by targeting the entire city (Scenario 1) versus the most populated pixels (Scenario 2), as described in Figure  4" • Line 217: It may be more inclusive to refer to "The availability of indoor cooling" versus air conditioning, since there are other technologies/non-mechanical options for cooling indoor spaces that may be appropriate (particularly in locations with lack of access to energy) • Line 265: Refers to Barcelona in Figure S2, but Figure S2 is Guangzhou • Figure S6: Some odd spelling mistakes, "tresholds" in titles and "botto" in the caption. We would like to express our sincere gratitude for taking the time to review our manuscript and provide us with valuable feedback. Your constructive comments and criticisms have helped us improve the quality of our research and the manuscript.
We are pleased to inform you that we have taken into account all of your suggestions and made the necessary revisions to the manuscript. Please find our detailed point-by-point response to your comments below, with your original comments highlighted in blue.
The major changes that we have made to the manuscript include: 1. Indicator change: we acknowledge that Land Surface Temperature (LST) may not be an adequate indicator for measuring population exposure to heat, and we have revised the definition to consider population exposure to LST extremes instead of population exposure to extreme heat. We have defined exposure as the number of days per year where LST exceeds a given threshold multiplied by the total urban population living in the same pixel, in person·day.

2.
Title change: we have revised the title of the manuscript as suggested.
3. Language revision: we have thoroughly reviewed and revised the manuscript to improve the clarity and readability of the language. We have also addressed all minor comments that you have provided.
Once again, we would like to express our sincere appreciation for your efforts and contribution to this research. We are confident that the revised manuscript will meet your expectations, and we look forward to your feedback.  2019), being published in such a high profile outlet (i.e. Nature), has unfortunately set back the understanding of heat exposure and heat stress in cities by readers who may not have the scientific context to understand the drawbacks or flaws in their method. I am opposed to any further such publications, and in its current state this submission claims that LST is useful in some practical sense to identify areas of increased urban heat exposure and the benefits of vegetation. Yet, there is little proof that this is so and a lot of clear reasons why we would expect it not to be. I do not want to see another high profile article (e.g. in Nature Communications in this case) conflate LST with the actual variables that impact heat exposure simply because LST is readily available and is therefore the basis of the method chosen.
A primary overall comment I have is that I think the use of the term "heat exposure" is not accurate. LST cannot be linked to heat exposure (indoor or outdoor) in a direct way. The use of this term when working with urban air temperature is more justified (but could still be questioned) because air is well mixed via turbulence, and therefore varies more slowly in space. Surface temperature, by contrast, varies at small spatial scales. Remotely-sensed surface temperature includes rooftop and tree top temperatures which are not of much relevance to outdoor heat exposure at ground level (pedestrians). I strongly recommend that the authors speak of surface temperature instead (and be clear about which surface temperature, e.g. see Stewart et al. 2021, Fig. 8). E.g., the title could be: "Reducing extreme surface temperature in cities through spatial analysis of urban greening" [Note: Manoli et al. (2020) make several comments about surface temperature being more useful than air temperature, which are only partially accurate and highly inaccurate if LST is the "surface temperature" metric they indicate. I suggest caution in using this non-peer reviewed publication by scientists who mostly have little urban climate background to support any claims.] This brings me to a second point. I don't think that spatial analysis of urban greening will reduce surface temperature (or heat exposure), but urban greening may well do so. Therefore, I suggest a title for this submission that is more accurate, such as: "Spatially-optimized urban greening for reduction of urban land surface temperature". "We changed the definition of population exposure to extreme heat areas, which are now defined as the number of days when heat exposure surpasses a specific threshold multiplied by the total urban population.  Fig. 8.) "(ii) offer monitoring and modelling systems of the urban thermal environment (hereafter defined as the urban thermoscape)" This is very vague. The claim seems to be made that LST can capture the "urban thermal environment" or "urban thermoscape": ". . . the design and evaluation of urban adaptation plans should be city-specific and based on the detailed assessment of the city thermoscape. To asses the latter requirements, in this study we focus our attention on the drivers of the urban LST gradients across different climate zones. . . . " I think more precision is required -which specific temperature or combination of temperatures are included in the "urban thermal environment" or "urban thermoscape"? It seems that LST is the answer in this submission. Yet, LST clearly misses several elements of importance to the "urban thermal environment", e.g. building walls, ground underneath trees, and includes others of less consequence (e.g. rooftops, depending on the application) and it ultimately a biased measure of the "urban thermal environment".
Even if the LST-air temperature relationship can be relied on (and there is only partial supporting evidence for this from only one study that Manoli et al. 2020 mention -Zhang et al. 2014 -there is little evidence I can find in the other papers they cite), this does not suggest that the LST differences induced by an intervention such as vegetation (or specifically trees) correlates to their air temperature differences. Trees, for example, affect air temperature if very complex ways (via at least four physical mechanisms -see work by Manoli  We express our gratitude to Reviewer 1 for acknowledging our efforts and revisions made to the manuscript. The feedback provided regarding the limitation of using LST for heat exposure was taken seriously, resulting in a revised definition of exposure that places greater emphasis on land surface temperature extremes, as reflected in both the title and text. The updated version emphasizes the urban residents residing in regions that encounter a significant number of days surpassing thresholds and are exposed to elevated land surface temperatures (LST).
We changed the title in Spatially-optimized urban greening for reduction of population exposure to land surface temperature extremes.
We would like to note that we eliminated the term thermoscape from the updated version since we reached a consensus that it was not fitting for this study. Our final goal and hope would be utilize LST observations along with climate models and other data to establish an accurate definition of an urban thermoscape in future studies.
"The study of LST allows for a more detailed and global representation of temperature patterns at both fine temporal (daily) and spatial (1km resolution) resolution, thereby enabling inter-city comparisons, that would not be feasible with air temperature data." The Tuholske et al. (2021) study uses an air temperature dataset that largely contradicts this point.
We appreciate the Reviewer for raising this point. In the Tuholske et al. (2021) study, the CHIRTS air temperature dataset was utilized, which has a spatial resolution of approximately 0.05°(equivalent to 5km). What we would like to point out in the manuscript is that obtaining measured or modeled air temperature data at a resolution of 1km for any city on a daily basis is currently unattainable.
Is satellite acquisition time reported in the Methods? I cannot find it.
We added this information in the Methods section.
Finally, the English language requires improvement in some of the newly-written sections.
We thank the Reviewer for this point. We revised the manuscript carefully improving the English and fixing all the typos.

3/5
I thank the reviewers for their effort and I think that they substantially improved the manuscript. They acknowledge several limitations including the ones that stem from relying on LST. For me it would be important that it is clear from the beginning how exposure is defined in this study and how it should be understood. This means that the definition in the abstract: "We determine exposure by calculating the number of days when heat exposure surpasses a specific threshold multiplied by the total urban population affected." should be replaced by the definition in the conclusions which is much clearer and much more meaningful "We defined exposure as the number of days per year where LST exceeds a heat exposure threshold multiplied by the total urban population exposed, in person days." In the abstract it also says: "Here, for the first time we implement a spatial regression model based on remote sensing data that is able to assess, with high accuracy, the population exposure to extreme heat in urban environments across 200 cities based on surface properties like vegetation cover and distance to water bodies." I don't agree with the statement "with high accuracy". This is purely based on the statistical fit as far as I understand. However, it does not pay attention to the fact that LST is a very specific and often inadequate indicator for "heat".
We extend our thanks to Reviewer 2 for recognizing the hard work and improvements made to the manuscript. We report the major changes in the following: • we added a definition of exposure in the abstract as suggested; • we removed the claim that we assess the exposure with high accuracy; • we concurred that land surface temperature (LST) is a highly specialized and sometimes insufficient measure for determining heat, and as a result, we revised the title and definitions. The updated version concentrates on the exposure of urban residents to elevated LST in areas where there is a substantial number of days and nights surpassing thresholds.
In the new version we talk about urban population expsosure to LST extremes instead of exposure to heat.

Reviewer 3
Thank you for the opportunity to review the revised manuscript. The revisions have greatly improved the study's overall framing and provided more nuance for its potential application in both research and practice. The discussion of the limitations of LST and references to other heat mitigation strategies, and the importance of considering indoor heat exposure, are welcome and better situates this study in current heat literature. My major feedback has been resolved, although I list several minor specific line comments below.
We express our gratitude to Reviewer 3 for acknowledging our efforts and the modifications we implemented. In our subsequent revisions, we have taken into account all of Reviewer 3's feedback.
Specific line feedback: • Line 52 (Figure 1): Consider a simple symbology for both Kyoto and Jeddah (since they are the cases listed) that help the reader identify them quickly on the world map and the graph.
• We added two arrows with texts to identify the two cities • Line 156 (Figure 4): Recommend briefly describing what Scenario 1 and 2 are in the caption (or giving them a more descriptive short-hand title) • We added a description of the scenarios in the caption of the Figure. • Line 190 ( Figure 5): Recommend clarifying caption for "C" to again make Scenario 1 and 2 more clear, such as: "C) Global NDVI increment in order to achieve the exposure reduction by targeting the entire city (Scenario 1) versus the most populated pixels (Scenario 2), as described in Figure 4" • We added a description of the scenarios in the caption of the Figure. • Line 217: It may be more inclusive to refer to "The availability of indoor cooling" versus air conditioning, since there are other technologies/non-mechanical options for cooling indoor spaces that may be appropriate (particularly in locations with lack of access to energy) • We thank Reviewer 3 for this comment and we changed the phrase accordingly.
• Line 265: Refers to Barcelona in Figure S2, but Figure S2 is Guangzhou.
• We made the correction for this reference.
• We really thank Reviewer 3 for noticing those typos that we corrected accordingly.

REVIEWERS' COMMENTS
Reviewer #1 (Remarks to the Author): The authors have taken the previous set of comments seriously and improved the manuscript. I still think that the word "exposure", e.g. in the title and abstract, is misleading in the context of LST. A significant component of LST, especially extremely hot LST in built up areas, derives from hot roof surface temperatures. However, pedestrians may be experiencing a largely shaded environment between the buildings. So pedestrians are not exposed to LST or at least to significant contributors to LST.
Conversely, I think the authors get it right in the following statement in the "Limitations" section: "For these reasons, in this research we do not focus on the population exposure to heat but to urban areas with high values of LST." That is, most accurately described, the authors are addressing the co-location of LST variation with population, not the exposure of the population to LST.
Spatially-optimized urban greening for reduction of population exposure to land surface temperature extremes

Reviewer 1
The authors have taken the previous set of comments seriously and improved the manuscript. I still think that the word "exposure", e.g. in the title and abstract, is misleading in the context of LST. A significant component of LST, especially extremely hot LST in built up areas, derives from hot roof surface temperatures. However, pedestrians may be experiencing a largely shaded environment between the buildings. So pedestrians are not exposed to LST or at least to significant contributors to LST. Conversely, I think the authors get it right in the following statement in the "Limitations" section: "For these reasons, in this research we do not focus on the population exposure to heat but to urban areas with high values of LST." That is, most accurately described, the authors are addressing the co-location of LST variation with population, not the exposure of the population to LST.
We want to thank Reviewer 1 again for recognizing our hard work in our research. In our study, we refer to the intersection or shared location of two variables, namely LST (derived from satellite) and population, as exposure. To clarify, we would say that "variable y is subject to exposure from variable" or "there exists exposure between variables x and y". Essentially, we treat LST as a variable and use the population layer as another variable. Furthermore, we acknowledge that LST may not always represent ground-level temperatures. However, we argue that high LST affects not only pedestrians but also individuals residing in higher floors of buildings, who experience higher temperatures than those on the ground floor. Therefore, we believe that the term exposure is appropriate for our purposes also in this case. Finally, we acknowledged and we agreed in the previous revisions about the limitation that LST may not be suitable for calculating heat estimates in the same manner as air temperature. However, we would like to emphasize that we utilize the term exposure of population to LST extremes to refer to the presence of both individuals and LST extremes within a given pixel.